Universität Wien

052113 VU Software Tools for Computational and Data Science (2024S)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work

Diese Lehrveranstaltung ist äquivalent zur VU "Software Tools and Libraries for Scientific Computing"
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Monday 04.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 06.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 11.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 13.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 18.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 20.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 08.04. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 10.04. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 15.04. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 17.04. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 22.04. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 24.04. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 29.04. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 06.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 08.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 13.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 15.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 22.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 27.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 29.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 03.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 05.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 10.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 12.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 17.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 19.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 24.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 26.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG

Information

Aims, contents and method of the course

We discuss software tools for computational and data science in the field of Linear Algebra, Ordinary Differential Equations, Sparse Linear Solvers, Optimization and Neural Networks including their foundational numerical algorithms. The students get familiar with using important computational software like BLAS and LAPACK, MPFR, and PETSc. Computational results are evaluated to ensure good performance. Hands-on experience is strengthened by in-depth discussions.

The students are expected to have good general programming skills, basic familiarity with programming in C and Python (other languages upon request), and experience in using GNU/Linux and Bash. The course builds upon the contents of the modules "Introduction to Numerical Computing" (NUM) and "Combinatorial and Numerical Algorithms" (CNA).

Assessment and permitted materials

The grading will be based on homeworks, two written exams (closed book), and projects. The projects shall be chosen with respect to the discussed topics.

Minimum requirements and assessment criteria

Presence is mandatory during the entire course. Each part (homework and projects with oral discussion on the computer, written exam) needs a score of at least 50%; grading: <50%=5, 50% up to 62.5%=4, up to 75%=3, up to 87,5%=2, 87,5% or better=1.

Examination topics

All topics of the lectures will be relevant for the exams.

Reading list

The lectures are accompanied by slides which point to additional relevant literature (supplied in the course). Textbooks etc. are not required.

Association in the course directory

Module: STL

Last modified: Su 03.03.2024 18:05